Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations12980
Missing cells332
Missing cells (%)0.1%
Duplicate rows180
Duplicate rows (%)1.4%
Total size in memory2.8 MiB
Average record size in memory224.0 B

Variable types

Categorical10
Boolean8
Numeric10

Alerts

Dataset has 180 (1.4%) duplicate rowsDuplicates
Bmi is highly overall correlated with WeightHigh correlation
Gender is highly overall correlated with HeightHigh correlation
Height is highly overall correlated with GenderHigh correlation
Weight is highly overall correlated with BmiHigh correlation
Depression is highly imbalanced (68.0%)Imbalance
ICU is highly imbalanced (72.5%)Imbalance
Drug_Abuse is highly imbalanced (77.0%)Imbalance
Mood_Disorder is highly imbalanced (90.6%)Imbalance
Diabetes is highly imbalanced (89.1%)Imbalance
Obesity is highly imbalanced (67.6%)Imbalance
Dementia is highly imbalanced (64.5%)Imbalance
IP_Visits is highly imbalanced (64.7%)Imbalance
Height has 310 (2.4%) missing valuesMissing
Pat_Pain_Score has 6282 (48.4%) zerosZeros
ER_Visits has 5035 (38.8%) zerosZeros

Reproduction

Analysis started2025-10-08 16:43:04.888300
Analysis finished2025-10-08 16:43:43.641238
Duration38.75 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Admit_Week
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Monday
2146 
Friday
2011 
Tuesday
1993 
Wednesday
1820 
Thursday
1813 
Other values (2)
3197 

Length

Max length9
Median length8
Mean length7.0973035
Min length6

Characters and Unicode

Total characters92123
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunday
2nd rowSunday
3rd rowSunday
4th rowSunday
5th rowSunday

Common Values

ValueCountFrequency (%)
Monday2146
16.5%
Friday2011
15.5%
Tuesday1993
15.4%
Wednesday1820
14.0%
Thursday1813
14.0%
Sunday1615
12.4%
Saturday1582
12.2%

Length

2025-10-08T16:43:43.786698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T16:43:43.912574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
monday2146
16.5%
friday2011
15.5%
tuesday1993
15.4%
wednesday1820
14.0%
thursday1813
14.0%
sunday1615
12.4%
saturday1582
12.2%

Most occurring characters

ValueCountFrequency (%)
d14800
16.1%
a14562
15.8%
y12980
14.1%
u7003
7.6%
e5633
 
6.1%
s5626
 
6.1%
n5581
 
6.1%
r5406
 
5.9%
T3806
 
4.1%
S3197
 
3.5%
Other values (7)13529
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)92123
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d14800
16.1%
a14562
15.8%
y12980
14.1%
u7003
7.6%
e5633
 
6.1%
s5626
 
6.1%
n5581
 
6.1%
r5406
 
5.9%
T3806
 
4.1%
S3197
 
3.5%
Other values (7)13529
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)92123
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d14800
16.1%
a14562
15.8%
y12980
14.1%
u7003
7.6%
e5633
 
6.1%
s5626
 
6.1%
n5581
 
6.1%
r5406
 
5.9%
T3806
 
4.1%
S3197
 
3.5%
Other values (7)13529
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)92123
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d14800
16.1%
a14562
15.8%
y12980
14.1%
u7003
7.6%
e5633
 
6.1%
s5626
 
6.1%
n5581
 
6.1%
r5406
 
5.9%
T3806
 
4.1%
S3197
 
3.5%
Other values (7)13529
14.7%

Admit_Month
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Dec
1333 
Jan
1289 
Mar
1244 
Apr
1133 
May
1125 
Other values (7)
6856 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters38940
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan
2nd rowJan
3rd rowJan
4th rowJan
5th rowJan

Common Values

ValueCountFrequency (%)
Dec1333
10.3%
Jan1289
9.9%
Mar1244
9.6%
Apr1133
8.7%
May1125
8.7%
Oct1029
7.9%
Jun1029
7.9%
Nov1022
7.9%
Feb968
7.5%
Sep968
7.5%
Other values (2)1840
14.2%

Length

2025-10-08T16:43:44.071885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec1333
10.3%
jan1289
9.9%
mar1244
9.6%
apr1133
8.7%
may1125
8.7%
oct1029
7.9%
jun1029
7.9%
nov1022
7.9%
feb968
7.5%
sep968
7.5%
Other values (2)1840
14.2%

Most occurring characters

ValueCountFrequency (%)
a3658
 
9.4%
e3269
 
8.4%
J3255
 
8.4%
u2869
 
7.4%
r2377
 
6.1%
M2369
 
6.1%
c2362
 
6.1%
n2318
 
6.0%
p2101
 
5.4%
A2036
 
5.2%
Other values (12)12326
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)38940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a3658
 
9.4%
e3269
 
8.4%
J3255
 
8.4%
u2869
 
7.4%
r2377
 
6.1%
M2369
 
6.1%
c2362
 
6.1%
n2318
 
6.0%
p2101
 
5.4%
A2036
 
5.2%
Other values (12)12326
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)38940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a3658
 
9.4%
e3269
 
8.4%
J3255
 
8.4%
u2869
 
7.4%
r2377
 
6.1%
M2369
 
6.1%
c2362
 
6.1%
n2318
 
6.0%
p2101
 
5.4%
A2036
 
5.2%
Other values (12)12326
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)38940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a3658
 
9.4%
e3269
 
8.4%
J3255
 
8.4%
u2869
 
7.4%
r2377
 
6.1%
M2369
 
6.1%
c2362
 
6.1%
n2318
 
6.0%
p2101
 
5.4%
A2036
 
5.2%
Other values (12)12326
31.7%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
F
6613 
M
6367 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12980
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
F6613
50.9%
M6367
49.1%

Length

2025-10-08T16:43:44.191131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T16:43:44.269693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f6613
50.9%
m6367
49.1%

Most occurring characters

ValueCountFrequency (%)
F6613
50.9%
M6367
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)12980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F6613
50.9%
M6367
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F6613
50.9%
M6367
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F6613
50.9%
M6367
49.1%

Marital_Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Single
6833 
Married
6147 

Length

Max length7
Median length6
Mean length6.4735747
Min length6

Characters and Unicode

Total characters84027
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Single6833
52.6%
Married6147
47.4%

Length

2025-10-08T16:43:44.368500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T16:43:44.445818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
single6833
52.6%
married6147
47.4%

Most occurring characters

ValueCountFrequency (%)
i12980
15.4%
e12980
15.4%
r12294
14.6%
S6833
8.1%
g6833
8.1%
n6833
8.1%
l6833
8.1%
M6147
7.3%
a6147
7.3%
d6147
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)84027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i12980
15.4%
e12980
15.4%
r12294
14.6%
S6833
8.1%
g6833
8.1%
n6833
8.1%
l6833
8.1%
M6147
7.3%
a6147
7.3%
d6147
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)84027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i12980
15.4%
e12980
15.4%
r12294
14.6%
S6833
8.1%
g6833
8.1%
n6833
8.1%
l6833
8.1%
M6147
7.3%
a6147
7.3%
d6147
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)84027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i12980
15.4%
e12980
15.4%
r12294
14.6%
S6833
8.1%
g6833
8.1%
n6833
8.1%
l6833
8.1%
M6147
7.3%
a6147
7.3%
d6147
7.3%

Insurance_Provider
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Medicare
10293 
Commercial
1838 
Medicaid
 
849

Length

Max length10
Median length8
Mean length8.2832049
Min length8

Characters and Unicode

Total characters107516
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedicare
2nd rowMedicare
3rd rowMedicare
4th rowMedicare
5th rowCommercial

Common Values

ValueCountFrequency (%)
Medicare10293
79.3%
Commercial1838
 
14.2%
Medicaid849
 
6.5%

Length

2025-10-08T16:43:44.537590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T16:43:44.616961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medicare10293
79.3%
commercial1838
 
14.2%
medicaid849
 
6.5%

Most occurring characters

ValueCountFrequency (%)
e23273
21.6%
i13829
12.9%
c12980
12.1%
a12980
12.1%
r12131
11.3%
d11991
11.2%
M11142
10.4%
m3676
 
3.4%
C1838
 
1.7%
o1838
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)107516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e23273
21.6%
i13829
12.9%
c12980
12.1%
a12980
12.1%
r12131
11.3%
d11991
11.2%
M11142
10.4%
m3676
 
3.4%
C1838
 
1.7%
o1838
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)107516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e23273
21.6%
i13829
12.9%
c12980
12.1%
a12980
12.1%
r12131
11.3%
d11991
11.2%
M11142
10.4%
m3676
 
3.4%
C1838
 
1.7%
o1838
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)107516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e23273
21.6%
i13829
12.9%
c12980
12.1%
a12980
12.1%
r12131
11.3%
d11991
11.2%
M11142
10.4%
m3676
 
3.4%
C1838
 
1.7%
o1838
 
1.7%

Tobacco_User
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Quit
6934 
Never
4131 
Yes
1810 
Not Asked
 
83
Passive
 
22

Length

Max length9
Median length4
Mean length4.2158706
Min length3

Characters and Unicode

Total characters54722
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd rowQuit
3rd rowNever
4th rowQuit
5th rowNever

Common Values

ValueCountFrequency (%)
Quit6934
53.4%
Never4131
31.8%
Yes1810
 
13.9%
Not Asked83
 
0.6%
Passive22
 
0.2%

Length

2025-10-08T16:43:44.709879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T16:43:44.807147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
quit6934
53.1%
never4131
31.6%
yes1810
 
13.9%
not83
 
0.6%
asked83
 
0.6%
passive22
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e10177
18.6%
t7017
12.8%
i6956
12.7%
Q6934
12.7%
u6934
12.7%
N4214
7.7%
v4153
7.6%
r4131
7.5%
s1937
 
3.5%
Y1810
 
3.3%
Other values (7)459
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)54722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e10177
18.6%
t7017
12.8%
i6956
12.7%
Q6934
12.7%
u6934
12.7%
N4214
7.7%
v4153
7.6%
r4131
7.5%
s1937
 
3.5%
Y1810
 
3.3%
Other values (7)459
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e10177
18.6%
t7017
12.8%
i6956
12.7%
Q6934
12.7%
u6934
12.7%
N4214
7.7%
v4153
7.6%
r4131
7.5%
s1937
 
3.5%
Y1810
 
3.3%
Other values (7)459
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e10177
18.6%
t7017
12.8%
i6956
12.7%
Q6934
12.7%
u6934
12.7%
N4214
7.7%
v4153
7.6%
r4131
7.5%
s1937
 
3.5%
Y1810
 
3.3%
Other values (7)459
 
0.8%

Depression
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
False
12224 
True
 
756
ValueCountFrequency (%)
False12224
94.2%
True756
 
5.8%
2025-10-08T16:43:44.878711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

ICU
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
False
12366 
True
 
614
ValueCountFrequency (%)
False12366
95.3%
True614
 
4.7%
2025-10-08T16:43:44.926453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Drug_Abuse
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
False
12494 
True
 
486
ValueCountFrequency (%)
False12494
96.3%
True486
 
3.7%
2025-10-08T16:43:44.974321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Mood_Disorder
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
False
12824 
True
 
156
ValueCountFrequency (%)
False12824
98.8%
True156
 
1.2%
2025-10-08T16:43:45.027020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Diabetes
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
False
12792 
True
 
188
ValueCountFrequency (%)
False12792
98.6%
True188
 
1.4%
2025-10-08T16:43:45.076019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Anxiety
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
False
10210 
True
2770 
ValueCountFrequency (%)
False10210
78.7%
True2770
 
21.3%
2025-10-08T16:43:45.125502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Obesity
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
False
12211 
True
 
769
ValueCountFrequency (%)
False12211
94.1%
True769
 
5.9%
2025-10-08T16:43:45.175194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Dementia
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
False
12109 
True
 
871
ValueCountFrequency (%)
False12109
93.3%
True871
 
6.7%
2025-10-08T16:43:45.223402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Age
Real number (ℝ)

Distinct668
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.61728
Minimum18.1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2025-10-08T16:43:45.324222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.1
5-th percentile45.3
Q163.4
median74.5
Q382.9
95-th percentile88.3
Maximum90
Range71.9
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation13.854881
Coefficient of variation (CV)0.19345724
Kurtosis0.77441392
Mean71.61728
Median Absolute Deviation (MAD)9.3
Skewness-0.9912293
Sum929592.3
Variance191.95774
MonotonicityNot monotonic
2025-10-08T16:43:45.475982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.469
 
0.5%
85.168
 
0.5%
87.364
 
0.5%
83.163
 
0.5%
8563
 
0.5%
8660
 
0.5%
82.660
 
0.5%
77.959
 
0.5%
85.257
 
0.4%
78.357
 
0.4%
Other values (658)12360
95.2%
ValueCountFrequency (%)
18.11
 
< 0.1%
18.52
< 0.1%
18.62
< 0.1%
18.71
 
< 0.1%
18.81
 
< 0.1%
193
< 0.1%
19.21
 
< 0.1%
19.31
 
< 0.1%
19.42
< 0.1%
19.71
 
< 0.1%
ValueCountFrequency (%)
9019
0.1%
89.934
0.3%
89.834
0.3%
89.737
0.3%
89.643
0.3%
89.546
0.4%
89.432
0.2%
89.336
0.3%
89.236
0.3%
89.138
0.3%

Bmi
Real number (ℝ)

High correlation 

Distinct3382
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.337493
Minimum14
Maximum54.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2025-10-08T16:43:45.627134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile19.0395
Q124.16
median28.81
Q335.1225
95-th percentile47.4605
Maximum54.47
Range40.47
Interquartile range (IQR)10.9625

Descriptive statistics

Standard deviation8.4656357
Coefficient of variation (CV)0.27904862
Kurtosis0.2871667
Mean30.337493
Median Absolute Deviation (MAD)5.24
Skewness0.79253815
Sum393780.66
Variance71.666989
MonotonicityNot monotonic
2025-10-08T16:43:45.775253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.47140
 
1.1%
1423
 
0.2%
27.0221
 
0.2%
2520
 
0.2%
27.4520
 
0.2%
2318
 
0.1%
25.2318
 
0.1%
25.1517
 
0.1%
26.4517
 
0.1%
30.2617
 
0.1%
Other values (3372)12669
97.6%
ValueCountFrequency (%)
1423
0.2%
14.022
 
< 0.1%
14.071
 
< 0.1%
14.091
 
< 0.1%
14.131
 
< 0.1%
14.311
 
< 0.1%
14.341
 
< 0.1%
14.451
 
< 0.1%
14.531
 
< 0.1%
14.541
 
< 0.1%
ValueCountFrequency (%)
54.47140
1.1%
54.468797511
 
< 0.1%
54.462304551
 
< 0.1%
54.461
 
< 0.1%
54.452865791
 
< 0.1%
54.451
 
< 0.1%
54.432
 
< 0.1%
54.421
 
< 0.1%
54.411
 
< 0.1%
54.404304871
 
< 0.1%

Weight
Real number (ℝ)

High correlation 

Distinct325
Distinct (%)2.5%
Missing22
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean190.99105
Minimum42
Maximum392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2025-10-08T16:43:45.939007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile112
Q1149
median182
Q3225
95-th percentile301
Maximum392
Range350
Interquartile range (IQR)76

Descriptive statistics

Standard deviation58.106992
Coefficient of variation (CV)0.30423935
Kurtosis0.53137003
Mean190.99105
Median Absolute Deviation (MAD)37
Skewness0.78162376
Sum2474862
Variance3376.4225
MonotonicityNot monotonic
2025-10-08T16:43:46.098788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157121
 
0.9%
180118
 
0.9%
181117
 
0.9%
174113
 
0.9%
170109
 
0.8%
158108
 
0.8%
149107
 
0.8%
172107
 
0.8%
185106
 
0.8%
146106
 
0.8%
Other values (315)11846
91.3%
ValueCountFrequency (%)
421
 
< 0.1%
491
 
< 0.1%
541
 
< 0.1%
621
 
< 0.1%
651
 
< 0.1%
671
 
< 0.1%
701
 
< 0.1%
735
< 0.1%
752
 
< 0.1%
765
< 0.1%
ValueCountFrequency (%)
39226
0.2%
3911
 
< 0.1%
3901
 
< 0.1%
3894
 
< 0.1%
3884
 
< 0.1%
3874
 
< 0.1%
3867
 
0.1%
3853
 
< 0.1%
3845
 
< 0.1%
3836
 
< 0.1%

Height
Real number (ℝ)

High correlation  Missing 

Distinct33
Distinct (%)0.3%
Missing310
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean66.366772
Minimum40
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2025-10-08T16:43:46.233455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile60
Q163
median66
Q370
95-th percentile73
Maximum76
Range36
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2967387
Coefficient of variation (CV)0.064742318
Kurtosis-0.11234985
Mean66.366772
Median Absolute Deviation (MAD)3
Skewness-0.09330381
Sum840867
Variance18.461963
MonotonicityNot monotonic
2025-10-08T16:43:46.360899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
661105
 
8.5%
671030
 
7.9%
641022
 
7.9%
621000
 
7.7%
63932
 
7.2%
70914
 
7.0%
68904
 
7.0%
65884
 
6.8%
69866
 
6.7%
72820
 
6.3%
Other values (23)3193
24.6%
ValueCountFrequency (%)
402
 
< 0.1%
442
 
< 0.1%
451
 
< 0.1%
472
 
< 0.1%
482
 
< 0.1%
492
 
< 0.1%
502
 
< 0.1%
512
 
< 0.1%
523
< 0.1%
535
< 0.1%
ValueCountFrequency (%)
76141
 
1.1%
75137
 
1.1%
74270
 
2.1%
73357
 
2.8%
72820
6.3%
71681
5.2%
70914
7.0%
69866
6.7%
68904
7.0%
671030
7.9%

Pulse
Real number (ℝ)

Distinct96
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.675347
Minimum48
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2025-10-08T16:43:46.498198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile58
Q168
median77
Q388
95-th percentile106
Maximum145
Range97
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.869041
Coefficient of variation (CV)0.18899237
Kurtosis0.53226018
Mean78.675347
Median Absolute Deviation (MAD)10
Skewness0.66436639
Sum1021206
Variance221.08837
MonotonicityNot monotonic
2025-10-08T16:43:46.635806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70523
 
4.0%
80465
 
3.6%
72455
 
3.5%
74389
 
3.0%
68356
 
2.7%
76352
 
2.7%
60346
 
2.7%
78337
 
2.6%
75324
 
2.5%
88315
 
2.4%
Other values (86)9118
70.2%
ValueCountFrequency (%)
4814
 
0.1%
4912
 
0.1%
5052
0.4%
5144
 
0.3%
5264
0.5%
5364
0.5%
5459
0.5%
5598
0.8%
5699
0.8%
57120
0.9%
ValueCountFrequency (%)
1453
< 0.1%
1443
< 0.1%
1412
 
< 0.1%
1406
< 0.1%
1391
 
< 0.1%
1381
 
< 0.1%
1373
< 0.1%
1365
< 0.1%
1353
< 0.1%
1343
< 0.1%

Pat_Pain_Score
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.949923
Minimum0
Maximum10
Zeros6282
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2025-10-08T16:43:46.743308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5707861
Coefficient of variation (CV)1.318404
Kurtosis0.54947379
Mean1.949923
Median Absolute Deviation (MAD)1
Skewness1.256532
Sum25310
Variance6.6089414
MonotonicityNot monotonic
2025-10-08T16:43:46.846245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
06282
48.4%
21525
 
11.7%
11278
 
9.8%
3819
 
6.3%
5741
 
5.7%
4696
 
5.4%
6486
 
3.7%
7475
 
3.7%
8432
 
3.3%
10125
 
1.0%
ValueCountFrequency (%)
06282
48.4%
11278
 
9.8%
21525
 
11.7%
3819
 
6.3%
4696
 
5.4%
5741
 
5.7%
6486
 
3.7%
7475
 
3.7%
8432
 
3.3%
9121
 
0.9%
ValueCountFrequency (%)
10125
 
1.0%
9121
 
0.9%
8432
 
3.3%
7475
 
3.7%
6486
 
3.7%
5741
5.7%
4696
5.4%
3819
6.3%
21525
11.7%
11278
9.8%

ER_Visits
Real number (ℝ)

Zeros 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6990755
Minimum0
Maximum13
Zeros5035
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2025-10-08T16:43:46.955276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2136203
Coefficient of variation (CV)1.3028381
Kurtosis4.4698819
Mean1.6990755
Median Absolute Deviation (MAD)1
Skewness1.949645
Sum22054
Variance4.9001147
MonotonicityNot monotonic
2025-10-08T16:43:47.068207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
05035
38.8%
12974
22.9%
21765
 
13.6%
31141
 
8.8%
4705
 
5.4%
5474
 
3.7%
6295
 
2.3%
7179
 
1.4%
8161
 
1.2%
983
 
0.6%
Other values (4)168
 
1.3%
ValueCountFrequency (%)
05035
38.8%
12974
22.9%
21765
 
13.6%
31141
 
8.8%
4705
 
5.4%
5474
 
3.7%
6295
 
2.3%
7179
 
1.4%
8161
 
1.2%
983
 
0.6%
ValueCountFrequency (%)
1327
 
0.2%
1239
 
0.3%
1140
 
0.3%
1062
 
0.5%
983
 
0.6%
8161
 
1.2%
7179
 
1.4%
6295
2.3%
5474
3.7%
4705
5.4%

IP_Visits
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
0
11619 
1
 
1094
2
 
267

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12980
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011619
89.5%
11094
 
8.4%
2267
 
2.1%

Length

2025-10-08T16:43:47.178383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T16:43:47.251379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
011619
89.5%
11094
 
8.4%
2267
 
2.1%

Most occurring characters

ValueCountFrequency (%)
011619
89.5%
11094
 
8.4%
2267
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)12980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
011619
89.5%
11094
 
8.4%
2267
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
011619
89.5%
11094
 
8.4%
2267
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
011619
89.5%
11094
 
8.4%
2267
 
2.1%

Chronic_Conditions
Real number (ℝ)

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6124037
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2025-10-08T16:43:47.341797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile17
Maximum27
Range26
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.8517986
Coefficient of variation (CV)0.73374204
Kurtosis1.9092186
Mean6.6124037
Median Absolute Deviation (MAD)3
Skewness1.3242662
Sum85829
Variance23.539949
MonotonicityNot monotonic
2025-10-08T16:43:47.460634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
11380
10.6%
51308
10.1%
41306
10.1%
21230
9.5%
31215
9.4%
61171
9.0%
71042
8.0%
8882
 
6.8%
9663
 
5.1%
10549
 
4.2%
Other values (17)2234
17.2%
ValueCountFrequency (%)
11380
10.6%
21230
9.5%
31215
9.4%
41306
10.1%
51308
10.1%
61171
9.0%
71042
8.0%
8882
6.8%
9663
5.1%
10549
 
4.2%
ValueCountFrequency (%)
2717
 
0.1%
2624
 
0.2%
2528
 
0.2%
2448
0.4%
2340
 
0.3%
2245
0.3%
2142
 
0.3%
2073
0.6%
19109
0.8%
1888
0.7%

Glucose
Real number (ℝ)

Distinct356
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.63451
Minimum58
Maximum423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2025-10-08T16:43:47.593874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile82
Q196
median127
Q3172
95-th percentile281
Maximum423
Range365
Interquartile range (IQR)76

Descriptive statistics

Standard deviation63.83273
Coefficient of variation (CV)0.44133816
Kurtosis2.3207425
Mean144.63451
Median Absolute Deviation (MAD)33
Skewness1.5146054
Sum1877356
Variance4074.6175
MonotonicityNot monotonic
2025-10-08T16:43:48.129781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97325
 
2.5%
99312
 
2.4%
98285
 
2.2%
96268
 
2.1%
95268
 
2.1%
94253
 
1.9%
93252
 
1.9%
92220
 
1.7%
91215
 
1.7%
90186
 
1.4%
Other values (346)10396
80.1%
ValueCountFrequency (%)
589
0.1%
5916
0.1%
6010
0.1%
619
0.1%
6212
0.1%
639
0.1%
6412
0.1%
659
0.1%
6619
0.1%
6713
0.1%
ValueCountFrequency (%)
4231
 
< 0.1%
4222
< 0.1%
4201
 
< 0.1%
4193
< 0.1%
4181
 
< 0.1%
4173
< 0.1%
4161
 
< 0.1%
4151
 
< 0.1%
4142
< 0.1%
4134
< 0.1%

Condition
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Heart_Failure
6774 
Pneumonia
6206 

Length

Max length13
Median length13
Mean length11.087519
Min length9

Characters and Unicode

Total characters143916
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPneumonia
2nd rowPneumonia
3rd rowHeart_Failure
4th rowHeart_Failure
5th rowHeart_Failure

Common Values

ValueCountFrequency (%)
Heart_Failure6774
52.2%
Pneumonia6206
47.8%

Length

2025-10-08T16:43:48.270043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T16:43:48.347014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
heart_failure6774
52.2%
pneumonia6206
47.8%

Most occurring characters

ValueCountFrequency (%)
e19754
13.7%
a19754
13.7%
r13548
9.4%
i12980
9.0%
u12980
9.0%
n12412
8.6%
_6774
 
4.7%
t6774
 
4.7%
H6774
 
4.7%
F6774
 
4.7%
Other values (4)25392
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)143916
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e19754
13.7%
a19754
13.7%
r13548
9.4%
i12980
9.0%
u12980
9.0%
n12412
8.6%
_6774
 
4.7%
t6774
 
4.7%
H6774
 
4.7%
F6774
 
4.7%
Other values (4)25392
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)143916
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e19754
13.7%
a19754
13.7%
r13548
9.4%
i12980
9.0%
u12980
9.0%
n12412
8.6%
_6774
 
4.7%
t6774
 
4.7%
H6774
 
4.7%
F6774
 
4.7%
Other values (4)25392
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)143916
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e19754
13.7%
a19754
13.7%
r13548
9.4%
i12980
9.0%
u12980
9.0%
n12412
8.6%
_6774
 
4.7%
t6774
 
4.7%
H6774
 
4.7%
F6774
 
4.7%
Other values (4)25392
17.6%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Discharged to Home
3994 
Home Health
3312 
Skilled Nursing Facility
2736 
Telehealth
2000 
Hospice
508 

Length

Max length24
Median length18
Mean length15.451002
Min length7

Characters and Unicode

Total characters200554
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSkilled Nursing Facility
2nd rowTelehealth
3rd rowSkilled Nursing Facility
4th rowTelehealth
5th rowTelehealth

Common Values

ValueCountFrequency (%)
Discharged to Home3994
30.8%
Home Health3312
25.5%
Skilled Nursing Facility2736
21.1%
Telehealth2000
15.4%
Hospice508
 
3.9%
Expired430
 
3.3%

Length

2025-10-08T16:43:48.441446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T16:43:48.534640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
home7306
24.6%
discharged3994
13.4%
to3994
13.4%
health3312
11.1%
skilled2736
 
9.2%
nursing2736
 
9.2%
facility2736
 
9.2%
telehealth2000
 
6.7%
hospice508
 
1.7%
expired430
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e24286
 
12.1%
16772
 
8.4%
i15876
 
7.9%
l15520
 
7.7%
a12042
 
6.0%
t12042
 
6.0%
o11808
 
5.9%
h11306
 
5.6%
H11126
 
5.5%
m7306
 
3.6%
Other values (17)62470
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)200554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e24286
 
12.1%
16772
 
8.4%
i15876
 
7.9%
l15520
 
7.7%
a12042
 
6.0%
t12042
 
6.0%
o11808
 
5.9%
h11306
 
5.6%
H11126
 
5.5%
m7306
 
3.6%
Other values (17)62470
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)200554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e24286
 
12.1%
16772
 
8.4%
i15876
 
7.9%
l15520
 
7.7%
a12042
 
6.0%
t12042
 
6.0%
o11808
 
5.9%
h11306
 
5.6%
H11126
 
5.5%
m7306
 
3.6%
Other values (17)62470
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)200554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e24286
 
12.1%
16772
 
8.4%
i15876
 
7.9%
l15520
 
7.7%
a12042
 
6.0%
t12042
 
6.0%
o11808
 
5.9%
h11306
 
5.6%
H11126
 
5.5%
m7306
 
3.6%
Other values (17)62470
31.1%

Cost_Of_Initial_Stay
Real number (ℝ)

Distinct12485
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7268.5442
Minimum9.4
Maximum117843.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2025-10-08T16:43:48.675022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.4
5-th percentile2199.094
Q13722.6475
median5579.405
Q38616.0425
95-th percentile17905.579
Maximum117843.12
Range117833.72
Interquartile range (IQR)4893.395

Descriptive statistics

Standard deviation6172.1291
Coefficient of variation (CV)0.84915615
Kurtosis31.457824
Mean7268.5442
Median Absolute Deviation (MAD)2199.49
Skewness4.0758482
Sum94345704
Variance38095177
MonotonicityNot monotonic
2025-10-08T16:43:48.818122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6416.299
 
0.1%
5632.079
 
0.1%
18310.119
 
0.1%
2777.429
 
0.1%
5672.999
 
0.1%
3530.258
 
0.1%
5960.248
 
0.1%
4302.364
 
< 0.1%
15001.584
 
< 0.1%
19337.134
 
< 0.1%
Other values (12475)12907
99.4%
ValueCountFrequency (%)
9.41
< 0.1%
11.911
< 0.1%
43.141
< 0.1%
51.661
< 0.1%
63.511
< 0.1%
96.421
< 0.1%
123.41
< 0.1%
128.341
< 0.1%
129.221
< 0.1%
138.991
< 0.1%
ValueCountFrequency (%)
117843.121
< 0.1%
99022.531
< 0.1%
81444.461
< 0.1%
78976.151
< 0.1%
71584.671
< 0.1%
70039.081
< 0.1%
69586.421
< 0.1%
66340.81
< 0.1%
63486.821
< 0.1%
63405.491
< 0.1%

Readmit30
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
0
10483 
1
2497 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12980
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010483
80.8%
12497
 
19.2%

Length

2025-10-08T16:43:48.960362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T16:43:49.029369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
010483
80.8%
12497
 
19.2%

Most occurring characters

ValueCountFrequency (%)
010483
80.8%
12497
 
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)12980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
010483
80.8%
12497
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
010483
80.8%
12497
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
010483
80.8%
12497
 
19.2%

Interactions

2025-10-08T16:43:41.305211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:15.250250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:18.710298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:23.033285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:29.215431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:31.863269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:34.413531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:36.641164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:38.504537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:40.075624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:41.423540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:15.471190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:18.981252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:23.918173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:29.471226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:32.234727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:34.567214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:36.832344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:38.662457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:40.196369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:41.554723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:15.732595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:19.388180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:24.786535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:29.696753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:32.991392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:34.711974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:37.050935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:38.829963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:40.325888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:41.692255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:16.102869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:19.872007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:25.583400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:29.920026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:33.313041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:34.858691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:37.281346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:39.018861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:40.444613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:41.820651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:16.754236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:20.450767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:26.133380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:30.156384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:33.449235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:35.030268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:37.501763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:39.197248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:40.570685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:41.943076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:17.154309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:20.892074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:26.703164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:30.395630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:33.616742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:35.176691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:37.669278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:39.364851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:40.698665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:42.078755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:17.427286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:21.325413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:27.030037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:30.700780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:33.774391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:35.314275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:37.832757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:39.548181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:40.815418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:42.210893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:17.792725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:21.656337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:27.294031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:30.928830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:33.969370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:35.455403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:37.997090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:39.719594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:40.933409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:42.630038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:18.116682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:22.034261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:28.498091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:31.173556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:34.122731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:35.594091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:38.162220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:39.844742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:41.054673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:42.770010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:18.364567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:22.398578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:28.777153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:31.530151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:34.268340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:36.378100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:38.324013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:39.952876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T16:43:41.181009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-08T16:43:49.135799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Admit_MonthAdmit_WeekAgeAnxietyBmiCare_Plan_Following_DischargeChronic_ConditionsConditionCost_Of_Initial_StayDementiaDepressionDiabetesDrug_AbuseER_VisitsGenderGlucoseHeightICUIP_VisitsInsurance_ProviderMarital_StatusMood_DisorderObesityPat_Pain_ScorePulseReadmit30Tobacco_UserWeight
Admit_Month1.0000.0310.0080.0170.0140.0390.0030.0790.0100.0200.0650.0280.0000.0210.0000.0140.0160.0000.0270.0180.0160.0210.0220.0180.0000.0200.0000.009
Admit_Week0.0311.0000.0030.0120.0110.0120.0170.0140.0070.0000.0000.0240.0000.0180.0200.0160.0140.0110.0260.0150.0050.0180.0290.0130.0140.0160.0180.019
Age0.0080.0031.0000.063-0.2920.1390.0620.1610.0100.0750.0630.0260.091-0.0240.046-0.066-0.1420.0310.0410.4770.1160.0310.192-0.140-0.1190.0470.172-0.335
Anxiety0.0170.0120.0631.0000.1910.0540.1670.0430.0380.0490.0340.0740.0760.0740.0170.0500.0310.0050.0380.0000.0120.0150.1670.0340.0410.0350.0380.196
Bmi0.0140.011-0.2920.1911.0000.0520.1350.1130.0290.0080.0340.0680.0380.0050.1300.217-0.0230.0000.0000.1210.0650.0320.4780.062-0.0510.0380.0410.895
Care_Plan_Following_Discharge0.0390.0120.1390.0540.0521.0000.0660.1280.1030.0510.0230.0180.0450.0620.0730.0240.0430.2780.0600.1820.1180.0100.0440.0230.1110.1120.0660.056
Chronic_Conditions0.0030.0170.0620.1670.1350.0661.0000.1790.0580.0920.1700.0610.0360.2300.0530.040-0.0560.0380.0540.0990.0110.0290.1050.117-0.0660.1020.0460.100
Condition0.0790.0140.1610.0430.1130.1280.1791.0000.0000.0260.0490.0000.0210.0970.0280.0720.0290.0410.0690.1210.0160.0140.0590.0550.1320.0750.0770.096
Cost_Of_Initial_Stay0.0100.0070.0100.0380.0290.1030.0580.0001.0000.0000.0330.0000.0000.0500.0000.087-0.0200.1120.0610.0310.0100.0000.007-0.0760.0290.0800.0180.016
Dementia0.0200.0000.0750.0490.0080.0510.0920.0260.0001.0000.0190.0000.0210.0700.0480.0210.0360.0000.0170.0670.0000.0110.0110.0330.0310.0360.0460.000
Depression0.0650.0000.0630.0340.0340.0230.1700.0490.0330.0191.0000.0040.0020.0560.0630.0060.0540.0250.0000.0250.0200.0050.0030.0890.0000.0000.0540.020
Diabetes0.0280.0240.0260.0740.0680.0180.0610.0000.0000.0000.0041.0000.0000.0280.0000.0400.0000.0000.0320.0150.0000.0100.0330.0290.0280.0250.0090.078
Drug_Abuse0.0000.0000.0910.0760.0380.0450.0360.0210.0000.0210.0020.0001.0000.0620.0000.0430.0140.0110.0000.0360.0140.0000.0120.0320.0210.0000.1860.009
ER_Visits0.0210.018-0.0240.0740.0050.0620.2300.0970.0500.0700.0560.0280.0621.0000.049-0.006-0.0490.0010.0680.0830.0800.0410.0810.114-0.0250.1580.050-0.014
Gender0.0000.0200.0460.0170.1300.0730.0530.0280.0000.0480.0630.0000.0000.0491.0000.0290.7340.0310.0000.0360.2440.0000.0140.0890.0000.0100.1970.276
Glucose0.0140.016-0.0660.0500.2170.0240.0400.0720.0870.0210.0060.0400.043-0.0060.0291.0000.0240.0370.0240.0420.0280.0000.089-0.0080.0100.0410.0290.209
Height0.0160.014-0.1420.031-0.0230.043-0.0560.029-0.0200.0360.0540.0000.014-0.0490.7340.0241.0000.0340.0280.0840.2050.0960.026-0.035-0.0180.0000.0870.392
ICU0.0000.0110.0310.0050.0000.2780.0380.0410.1120.0000.0250.0000.0110.0010.0310.0370.0341.0000.0620.0000.0120.0000.0000.0190.1050.0140.0080.026
IP_Visits0.0270.0260.0410.0380.0000.0600.0540.0690.0610.0170.0000.0320.0000.0680.0000.0240.0280.0621.0000.0160.0250.0000.0110.0400.0190.0550.0170.000
Insurance_Provider0.0180.0150.4770.0000.1210.1820.0990.1210.0310.0670.0250.0150.0360.0830.0360.0420.0840.0000.0161.0000.1610.0170.0700.0940.0690.0640.1630.145
Marital_Status0.0160.0050.1160.0120.0650.1180.0110.0160.0100.0000.0200.0000.0140.0800.2440.0280.2050.0120.0250.1611.0000.0320.0110.0490.0180.0260.1020.123
Mood_Disorder0.0210.0180.0310.0150.0320.0100.0290.0140.0000.0110.0050.0100.0000.0410.0000.0000.0960.0000.0000.0170.0321.0000.0130.0210.0230.0260.0180.009
Obesity0.0220.0290.1920.1670.4780.0440.1050.0590.0070.0110.0030.0330.0120.0810.0140.0890.0260.0000.0110.0700.0110.0131.0000.0480.0000.0180.0170.460
Pat_Pain_Score0.0180.013-0.1400.0340.0620.0230.1170.055-0.0760.0330.0890.0290.0320.1140.089-0.008-0.0350.0190.0400.0940.0490.0210.0481.0000.0230.0370.0610.046
Pulse0.0000.014-0.1190.041-0.0510.111-0.0660.1320.0290.0310.0000.0280.021-0.0250.0000.010-0.0180.1050.0190.0690.0180.0230.0000.0231.0000.0400.026-0.052
Readmit300.0200.0160.0470.0350.0380.1120.1020.0750.0800.0360.0000.0250.0000.1580.0100.0410.0000.0140.0550.0640.0260.0260.0180.0370.0401.0000.0530.033
Tobacco_User0.0000.0180.1720.0380.0410.0660.0460.0770.0180.0460.0540.0090.1860.0500.1970.0290.0870.0080.0170.1630.1020.0180.0170.0610.0260.0531.0000.033
Weight0.0090.019-0.3350.1960.8950.0560.1000.0960.0160.0000.0200.0780.009-0.0140.2760.2090.3920.0260.0000.1450.1230.0090.4600.046-0.0520.0330.0331.000

Missing values

2025-10-08T16:43:43.003039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-08T16:43:43.306208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-08T16:43:43.543318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Admit_WeekAdmit_MonthGenderMarital_StatusInsurance_ProviderTobacco_UserDepressionICUDrug_AbuseMood_DisorderDiabetesAnxietyObesityDementiaAgeBmiWeightHeightPulsePat_Pain_ScoreER_VisitsIP_VisitsChronic_ConditionsGlucoseConditionCare_Plan_Following_DischargeCost_Of_Initial_StayReadmit30
0SundayJanFMarriedMedicareNeverNoNoNoYesNoNoNoYes76.645.89000243.061.0750508113PneumoniaSkilled Nursing Facility8004.801
1SundayJanMMarriedMedicareQuitNoNoNoNoNoNoNoNo81.629.44000182.066.01002007109PneumoniaTelehealth3205.120
2SundayJanFSingleMedicareNeverNoNoNoNoNoNoNoNo87.920.51000102.059.076000698Heart_FailureSkilled Nursing Facility15694.260
3SundayJanMMarriedMedicareQuitNoNoNoNoNoNoNoNo74.326.93000172.067.0620307135Heart_FailureTelehealth10014.290
4SundayJanFSingleCommercialNeverNoNoNoNoNoNoNoNo37.333.72000235.070.0830002126Heart_FailureTelehealth8798.150
5SundayJanFSingleMedicareNeverNoNoNoNoNoNoNoNo85.230.55000162.061.0862007127PneumoniaTelehealth6068.430
6SundayJanFSingleMedicareQuitNoNoNoNoNoNoNoNo85.632.03000181.0NaN86500599PneumoniaHome Health4695.700
7SundayJanFSingleMedicaidQuitNoNoNoNoNoYesYesNo28.143.12806260.065.0686207134PneumoniaHome Health1704.470
8SundayJanMSingleCommercialYesNoNoNoNoNoNoNoNo44.528.73000212.072.062120494PneumoniaTelehealth2853.380
9SundayJanFSingleMedicareNeverNoNoNoNoNoNoNoNo72.422.23000134.065.0722104132PneumoniaTelehealth3028.250
Admit_WeekAdmit_MonthGenderMarital_StatusInsurance_ProviderTobacco_UserDepressionICUDrug_AbuseMood_DisorderDiabetesAnxietyObesityDementiaAgeBmiWeightHeightPulsePat_Pain_ScoreER_VisitsIP_VisitsChronic_ConditionsGlucoseConditionCare_Plan_Following_DischargeCost_Of_Initial_StayReadmit30
12970SaturdayDecFMarriedMedicareQuitNoNoNoNoNoNoNoNo70.132.62215.068.06069010162Heart_FailureDischarged to Home2751.320
12971SaturdayDecMSingleMedicareQuitNoNoNoNoNoNoYesNo55.636.21274.073.090110894Heart_FailureDischarged to Home4234.290
12972SaturdayDecMMarriedMedicareNeverNoNoNoNoNoNoNoNo68.828.20202.071.0620208159Heart_FailureHome Health13748.770
12973SaturdayDecFSingleMedicareQuitNoNoNoNoNoNoNoYes87.422.73137.065.0101050992Heart_FailureSkilled Nursing Facility4528.770
12974SaturdayDecMSingleMedicareQuitNoNoNoNoNoNoNoNo83.031.01210.069.064300790PneumoniaDischarged to Home3522.420
12975SaturdayDecMMarriedMedicareQuitNoYesNoNoNoNoNoNo65.031.08192.066.075320496Heart_FailureDischarged to Home5359.670
12976SaturdayDecFSingleMedicaidQuitNoNoNoNoNoNoYesNo59.454.47313.062.01054307157PneumoniaDischarged to Home5536.300
12977SaturdayDecMSingleMedicareQuitNoNoNoNoNoNoNoNo78.220.96155.072.0820105203PneumoniaDischarged to Home5427.930
12978SaturdayDecMMarriedMedicareQuitNoNoNoNoNoNoNoNo84.829.09181.066.0882305138PneumoniaHospice3112.010
12979SaturdayDecFSingleMedicareYesNoNoNoNoNoNoNoNo72.021.49121.063.0645107106PneumoniaHome Health6903.670

Duplicate rows

Most frequently occurring

Admit_WeekAdmit_MonthGenderMarital_StatusInsurance_ProviderTobacco_UserDepressionICUDrug_AbuseMood_DisorderDiabetesAnxietyObesityDementiaAgeBmiWeightHeightPulsePat_Pain_ScoreER_VisitsIP_VisitsChronic_ConditionsGlucoseConditionCare_Plan_Following_DischargeCost_Of_Initial_StayReadmit30# duplicates
44MondayJunFSingleMedicareQuitNoNoNoNoNoNoNoNo66.929.10000180.066.079040984Heart_FailureHome Health5632.0719
86SundayAprMSingleMedicaidNeverNoNoNoNoNoNoNoNo52.327.45000191.070.0814001108Heart_FailureDischarged to Home5672.9919
98SundayMarMSingleMedicareYesNoNoNoYesNoNoNoNo88.718.20000134.072.0109030497Heart_FailureHome Health2777.4219
13FridayJulMMarriedMedicaidQuitNoNoNoNoNoNoNoNo51.334.39000261.073.05900010278Heart_FailureHome Health3530.2518
30MondayAprMSingleMedicareYesYesNoNoNoNoYesNoNo65.828.84000195.069.0895801887Heart_FailureDischarged to Home6416.2918
76SaturdayJunMSingleMedicareYesYesNoNoNoNoYesNoNo66.027.36000185.069.0650301694PneumoniaTelehealth18310.1118
0FridayAprFSingleMedicareQuitNoNoNoNoNoNoNoNo67.517.48000108.066.0720201119Heart_FailureSkilled Nursing Facility14321.2814
2FridayDecFMarriedMedicareQuitNoNoNoNoNoYesNoNo85.130.84000169.062.05383015376Heart_FailureDischarged to Home3607.2914
5FridayDecMSingleMedicareQuitNoNoNoYesNoNoNoNo58.053.88255370.068.01030103184Heart_FailureDischarged to Home9978.4114
8FridayFebFSingleMedicareYesNoNoNoNoNoYesYesYes59.943.06000275.067.0590101981Heart_FailureDischarged to Home3477.6514